{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from torchvision import transforms, utils\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "import torch.optim as optim\n",
    "import torch.backends.cudnn as cudnn\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from torch.autograd import Variable\n",
    "import torch.utils.data as Data\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "from skimage import io\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class BasicBlock(nn.Module):\n",
    "    expansion = 1\n",
    "\n",
    "    def __init__(self, in_planes, planes, stride=1):\n",
    "        super(BasicBlock, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)\n",
    "        self.bn1 = nn.BatchNorm2d(planes)\n",
    "        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)\n",
    "        self.bn2 = nn.BatchNorm2d(planes)\n",
    "\n",
    "        self.shortcut = nn.Sequential()\n",
    "        if stride != 1 or in_planes != self.expansion*planes:\n",
    "            self.shortcut = nn.Sequential(\n",
    "                nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False),\n",
    "                nn.BatchNorm2d(self.expansion*planes)\n",
    "            )\n",
    "\n",
    "    def forward(self, x):\n",
    "        out = F.relu(self.bn1(self.conv1(x)))\n",
    "        out = self.bn2(self.conv2(out))\n",
    "        out += self.shortcut(x)\n",
    "        out = F.relu(out)\n",
    "        return out\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ResNet(nn.Module):\n",
    "    def __init__(self, block, num_blocks, num_output=4):\n",
    "        super(ResNet,self).__init__()\n",
    "        self.in_planes = 64\n",
    "        \n",
    "        self.conv1 = nn.Conv2d(3,64,kernel_size=3, stride=1, padding=1, bias=False)\n",
    "        self.bn1 = nn.BatchNorm2d(64)\n",
    "        self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)\n",
    "        self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)\n",
    "        self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)\n",
    "        self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)\n",
    "        #self.linear = nn.Linear(512*block.expansion, num_output) \n",
    "        self.linear = nn.Linear(153600, num_output)     \n",
    "\n",
    "        \n",
    "    def _make_layer(self, block, planes, num_blocks, stride):\n",
    "        strides = [stride] + [1]*(num_blocks-1)\n",
    "        layers = []\n",
    "        for stride in strides:\n",
    "            layers.append(block(self.in_planes, planes, stride))\n",
    "            self.in_planes = planes * block.expansion\n",
    "        return nn.Sequential(*layers)\n",
    "\n",
    "    def forward(self, x):\n",
    "        out = F.relu(self.bn1(self.conv1(x)))\n",
    "        out = self.layer1(out)\n",
    "        out = self.layer2(out)\n",
    "        out = self.layer3(out)\n",
    "        out = self.layer4(out)\n",
    "        out = F.avg_pool2d(out, 4)\n",
    "        out = out.view(out.size(0), -1)\n",
    "        out = self.linear(out)\n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DroNet(nn.Module):\n",
    "    def __init__(self,block,num_output=4):\n",
    "        super(DroNet,self).__init__()\n",
    "        self.in_planes = 32\n",
    "        \n",
    "        self.conv1 = nn.Conv2d(3,32,kernel_size=5, stride=2, padding=1, bias=False)\n",
    "        self.pool = nn.MaxPool2d(3,stride=2)\n",
    "        self.bn1 = nn.BatchNorm2d(32)\n",
    "        self.layer1 = self._make_layer(block,32,stride=2)\n",
    "        self.layer2 = self._make_layer(block,64,stride=2)\n",
    "        self.layer3 = self._make_layer(block,128,stride=2)\n",
    "        self.linear = nn.Linear(1920, num_output)\n",
    "        \n",
    "    def _make_layer(self, block, planes, stride=2):\n",
    "        layer = block(self.in_planes, planes, stride)\n",
    "        self.in_planes = planes*block.expansion\n",
    "\n",
    "        return nn.Sequential(layer)\n",
    "    \n",
    "    def forward(self, x):\n",
    "        out = F.relu(self.bn1(self.conv1(x)))\n",
    "        out = self.layer1(out)\n",
    "        out = self.layer2(out)\n",
    "        out = self.layer3(out)\n",
    "        out = F.avg_pool2d(out, 4)\n",
    "        out = out.view(out.size(0), -1)\n",
    "        out = self.linear(out)\n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ResNet18():\n",
    "    return ResNet(BasicBlock, [2,2,2,2])\n",
    "def image_loader(image_name):\n",
    "    image = Image.open(image_name).convert('RGB')\n",
    "    image = image.resize((320,240))\n",
    "    return image\n",
    "transform = transforms.ToTensor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "image = Image.open('./gate_img/3000.jpg')\n",
    "image = image.resize((320,240))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
    "#device = 'cpu'\n",
    "start_epoch = 0\n",
    "#net = ResNet18().to(device)\n",
    "net = DroNet(BasicBlock).to(device)\n",
    "if device == 'cuda':\n",
    "    net = torch.nn.DataParallel(net)\n",
    "    cudnn.benchmark = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "class GateDataset(Dataset):\n",
    "    def __init__(self, txt, transform, loader,train=True):\n",
    "        f=open(txt, 'r')\n",
    "        data = []\n",
    "        count = 0\n",
    "        for line in f:\n",
    "            line = line.strip('\\n')\n",
    "            line = line.rstrip()\n",
    "            words = line.split()\n",
    "            label = np.array([float(words[1]),float(words[2]),float(words[3]),float(words[4])])\n",
    "            label = torch.from_numpy(label)\n",
    "            if train:\n",
    "                if (count%1==0)&(count <= 2500):\n",
    "                    data.append((words[0],label))\n",
    "                count+=1\n",
    "            else:\n",
    "                if (count%1==0)&(count > 2500):\n",
    "                    data.append((words[0],label))\n",
    "                count+=1\n",
    "        self.data = data\n",
    "        self.transform = transform\n",
    "        self.loader = loader\n",
    "        \n",
    "    \n",
    "    def __getitem__(self, index):\n",
    "        img_file, label = self.data[index]\n",
    "        img_file = './gate_img/'+img_file\n",
    "        img = self.loader(img_file)\n",
    "        if self.transform is not None:\n",
    "            img = self.transform(img)\n",
    "        return img, label\n",
    "    \n",
    "    def __len__(self):\n",
    "        return len(self.data)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = GateDataset(txt='./gate_img/ground_truth.txt',transform=transforms.ToTensor(),loader=image_loader,train=True)\n",
    "test_data = GateDataset(txt='./gate_img/ground_truth.txt',transform=transforms.ToTensor(),loader=image_loader,train=False)\n",
    "data_loader = DataLoader(train_data, batch_size=100,shuffle=True)\n",
    "data_loader_test = DataLoader(test_data, batch_size=20,shuffle=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "26\n",
      "torch.Size([100, 3, 240, 320])\n"
     ]
    }
   ],
   "source": [
    "print(len(data_loader))\n",
    "images, labels = next(iter(data_loader))\n",
    "print(images.size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "26\n",
      "torch.Size([100, 3, 240, 320])\n",
      "torch.Size([100, 32, 120, 160])\n",
      "torch.Size([100, 32, 60, 80])\n",
      "torch.Size([100, 64, 30, 40])\n",
      "torch.Size([100, 128, 15, 20])\n",
      "torch.Size([100, 128, 3, 5])\n",
      "torch.Size([100, 1920])\n",
      "torch.Size([100, 4])\n"
     ]
    }
   ],
   "source": [
    "global in_planes\n",
    "in_planes = 32\n",
    "def _make_layer(block, planes, stride=2):\n",
    "    global in_planes\n",
    "    layer = block(in_planes, planes, stride)\n",
    "    in_planes = planes*block.expansion\n",
    "    return nn.Sequential(layer)\n",
    "\n",
    "print(len(data_loader))\n",
    "images, labels = next(iter(data_loader))\n",
    "print(images.size())\n",
    "conv1 = nn.Conv2d(3,32,kernel_size=5, stride=2, padding=2, bias=False)\n",
    "bn1 = nn.BatchNorm2d(32)\n",
    "out = F.relu(bn1(conv1(images)))\n",
    "layer1 = _make_layer(BasicBlock,32,stride=2)\n",
    "print(out.size())\n",
    "out = layer1(out)\n",
    "print(out.size())\n",
    "layer2 = _make_layer(BasicBlock,64,stride=2)\n",
    "out = layer2(out)\n",
    "print(out.size())\n",
    "layer3 = _make_layer(BasicBlock,128,stride=2)\n",
    "out = layer3(out)\n",
    "print(out.size())\n",
    "out = F.avg_pool2d(out, 4)\n",
    "print(out.size())\n",
    "out = out.view(out.size(0), -1)\n",
    "print(out.size())\n",
    "linear = nn.Linear(1920, 4)\n",
    "out = linear(out)\n",
    "print(out.size())\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "26\n",
      "torch.Size([2, 3, 240, 320])\n",
      "torch.Size([2, 64, 240, 320])\n",
      "torch.Size([2, 128, 120, 160])\n",
      "torch.Size([2, 256, 60, 80])\n",
      "torch.Size([2, 512, 30, 40])\n",
      "torch.Size([2, 512, 7, 10])\n",
      "torch.Size([2, 35840])\n",
      "torch.Size([2, 4])\n"
     ]
    }
   ],
   "source": [
    "num_block = [2,2,2,2]\n",
    "global in_planes\n",
    "in_planes = 64\n",
    "def _make_layer(block, planes, num_blocks, stride):\n",
    "    strides = [stride] + [1]*(num_blocks-1)\n",
    "    layers = []\n",
    "    global in_planes\n",
    "    for stride in strides:\n",
    "        layers.append(block(in_planes, planes, stride))\n",
    "        in_planes = planes * block.expansion\n",
    "    return nn.Sequential(*layers)\n",
    "\n",
    "\n",
    "\n",
    "print(len(data_loader))\n",
    "#images, labels = next(iter(data_loader))\n",
    "images = torch.randn(2,3,240,320)\n",
    "print(images.size())\n",
    "conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)\n",
    "bn1 = nn.BatchNorm2d(64)\n",
    "out = F.relu(bn1(conv1(images)))\n",
    "layer1 = _make_layer(BasicBlock,64,num_block[0],stride=1)\n",
    "\n",
    "out = layer1(out)\n",
    "print(out.size())\n",
    "layer2 = _make_layer(BasicBlock,128,num_block[1],stride=2)\n",
    "out = layer2(out)\n",
    "print(out.size())\n",
    "layer3 = _make_layer(BasicBlock,256,num_block[2],stride=2)\n",
    "out = layer3(out)\n",
    "print(out.size())\n",
    "layer4 = _make_layer(BasicBlock,512,num_block[3],stride=2)\n",
    "out = layer4(out)\n",
    "print(out.size())\n",
    "out = F.avg_pool2d(out, 4)\n",
    "print(out.size())\n",
    "out = out.view(out.size(0), -1)\n",
    "print(out.size())\n",
    "linear = nn.Linear(35840, 4)\n",
    "out = linear(out)\n",
    "print(out.size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = optim.SGD(net.parameters(), lr=0.002, momentum=0.9, weight_decay=5e-4)\n",
    "scheduler = optim.lr_scheduler.MultiStepLR(optimizer, milestones=[50, 150], gamma=0.1, last_epoch=-1)\n",
    "#optimizer = torch.optim.SGD(net.parameters(), lr=0.01)\n",
    "#criterion = nn.SmoothL1Loss()\n",
    "criterion = nn.MSELoss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(epoch, net):\n",
    "    #print('\\nEpoch: {} ==> lr: {}'.format(epoch, scheduler.get_last_lr()))\n",
    "    net.train()\n",
    "    train_loss = 0\n",
    "    for batch_idx, (inputs, targets) in enumerate(data_loader):\n",
    "        inputs = torch.tensor(inputs, dtype=torch.float32).to(device)\n",
    "        targets = torch.tensor(targets, dtype=torch.float32).to(device)\n",
    "        optimizer.zero_grad()\n",
    "        outputs = net(inputs)\n",
    "        loss = criterion(outputs, targets)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        train_loss += loss.item()\n",
    "        train_loss = (train_loss/(batch_idx+1))\n",
    "        print(\"the trainning loss is (%-10.3f): \" %(train_loss))\n",
    "def test(epoch, net):\n",
    "    net.eval()\n",
    "    test_loss = 0\n",
    "    with torch.no_grad():\n",
    "        for batch_idx, (inputs, targets) in enumerate(data_loader_test):\n",
    "            inputs = torch.tensor(inputs, dtype=torch.float32).to(device)\n",
    "            targets = torch.tensor(targets, dtype=torch.float32).to(device)\n",
    "            outputs = net(inputs) \n",
    "            loss = criterion(outputs, targets)\n",
    "            test_loss += loss.item()\n",
    "            test_loss = test_loss/(batch_idx+1)\n",
    "            print(\"the test loss is (%-10.3f): \" %(test_loss))\n",
    "\n",
    "\n",
    "\n",
    "            \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==> Training parameters:\n",
      "        start_epoch = 1\n",
      "        lr @epoch=0 = 0.1\n",
      "==> Starting training...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/colin/anaconda3/envs/deep_uncertainty/lib/python3.6/site-packages/torch/optim/lr_scheduler.py:122: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\n",
      "  \"https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\", UserWarning)\n",
      "/home/colin/anaconda3/envs/deep_uncertainty/lib/python3.6/site-packages/ipykernel_launcher.py:6: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  \n",
      "/home/colin/anaconda3/envs/deep_uncertainty/lib/python3.6/site-packages/ipykernel_launcher.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  import sys\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the trainning loss is (2.927     ): \n",
      "the trainning loss is (2.908     ): \n",
      "the trainning loss is (1.845     ): \n",
      "the trainning loss is (1.065     ): \n",
      "the trainning loss is (0.637     ): \n",
      "the trainning loss is (0.390     ): \n",
      "the trainning loss is (0.242     ): \n",
      "the trainning loss is (0.158     ): \n",
      "the trainning loss is (0.093     ): \n",
      "the trainning loss is (0.098     ): \n",
      "the trainning loss is (0.102     ): \n",
      "the trainning loss is (0.092     ): \n",
      "the trainning loss is (0.093     ): \n",
      "the trainning loss is (0.075     ): \n",
      "the trainning loss is (0.064     ): \n",
      "the trainning loss is (0.046     ): \n",
      "the trainning loss is (0.032     ): \n",
      "the trainning loss is (0.034     ): \n",
      "the trainning loss is (0.038     ): \n",
      "the trainning loss is (0.034     ): \n",
      "the trainning loss is (0.031     ): \n",
      "the trainning loss is (0.030     ): \n",
      "the trainning loss is (0.021     ): \n",
      "the trainning loss is (0.021     ): \n",
      "the trainning loss is (0.022     ): \n",
      "the trainning loss is (0.010     ): \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/colin/anaconda3/envs/deep_uncertainty/lib/python3.6/site-packages/ipykernel_launcher.py:21: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "/home/colin/anaconda3/envs/deep_uncertainty/lib/python3.6/site-packages/ipykernel_launcher.py:22: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the test loss is (1.243     ): \n",
      "the test loss is (1.218     ): \n",
      "the test loss is (0.810     ): \n",
      "the test loss is (0.524     ): \n",
      "the test loss is (0.374     ): \n",
      "the test loss is (0.302     ): \n",
      "the test loss is (0.248     ): \n",
      "the test loss is (0.244     ): \n",
      "the test loss is (0.225     ): \n",
      "the test loss is (0.169     ): \n",
      "the test loss is (0.146     ): \n",
      "the test loss is (0.137     ): \n",
      "the test loss is (0.111     ): \n",
      "the test loss is (0.114     ): \n",
      "the test loss is (0.090     ): \n",
      "the test loss is (0.079     ): \n",
      "the test loss is (0.074     ): \n",
      "the test loss is (0.058     ): \n",
      "the test loss is (0.046     ): \n",
      "the test loss is (0.030     ): \n",
      "the test loss is (0.034     ): \n",
      "the test loss is (0.035     ): \n",
      "the test loss is (0.039     ): \n",
      "the test loss is (0.042     ): \n",
      "the test loss is (0.043     ): \n",
      "the test loss is (0.041     ): \n",
      "the test loss is (0.030     ): \n",
      "the test loss is (0.022     ): \n",
      "the test loss is (0.018     ): \n",
      "the test loss is (0.013     ): \n",
      "the test loss is (0.007     ): \n",
      "the test loss is (0.006     ): \n",
      "the test loss is (0.014     ): \n",
      "the test loss is (0.022     ): \n",
      "the test loss is (0.027     ): \n",
      "the test loss is (0.045     ): \n",
      "the test loss is (0.054     ): \n",
      "the test loss is (0.065     ): \n",
      "the trainning loss is (0.626     ): \n",
      "the trainning loss is (0.623     ): \n",
      "the trainning loss is (0.430     ): \n",
      "the trainning loss is (0.224     ): \n",
      "the trainning loss is (0.122     ): \n",
      "the trainning loss is (0.087     ): \n",
      "the trainning loss is (0.063     ): \n",
      "the trainning loss is (0.065     ): \n",
      "the trainning loss is (0.055     ): \n",
      "the trainning loss is (0.047     ): \n",
      "the trainning loss is (0.049     ): \n",
      "the trainning loss is (0.034     ): \n",
      "the trainning loss is (0.032     ): \n",
      "the trainning loss is (0.027     ): \n",
      "the trainning loss is (0.027     ): \n",
      "the trainning loss is (0.025     ): \n",
      "the trainning loss is (0.018     ): \n",
      "the trainning loss is (0.021     ): \n",
      "the trainning loss is (0.015     ): \n",
      "the trainning loss is (0.018     ): \n",
      "the trainning loss is (0.013     ): \n",
      "the trainning loss is (0.010     ): \n",
      "the trainning loss is (0.009     ): \n",
      "the trainning loss is (0.011     ): \n",
      "the trainning loss is (0.011     ): \n",
      "the trainning loss is (0.002     ): \n",
      "the test loss is (0.325     ): \n",
      "the test loss is (0.443     ): \n",
      "the test loss is (0.319     ): \n",
      "the test loss is (0.184     ): \n",
      "the test loss is (0.117     ): \n",
      "the test loss is (0.083     ): \n",
      "the test loss is (0.062     ): \n",
      "the test loss is (0.075     ): \n",
      "the test loss is (0.083     ): \n",
      "the test loss is (0.048     ): \n",
      "the test loss is (0.037     ): \n",
      "the test loss is (0.034     ): \n",
      "the test loss is (0.027     ): \n",
      "the test loss is (0.039     ): \n",
      "the test loss is (0.020     ): \n",
      "the test loss is (0.016     ): \n",
      "the test loss is (0.015     ): \n",
      "the test loss is (0.011     ): \n",
      "the test loss is (0.017     ): \n",
      "the test loss is (0.030     ): \n",
      "the test loss is (0.042     ): \n",
      "the test loss is (0.056     ): \n",
      "the test loss is (0.067     ): \n",
      "the test loss is (0.071     ): \n",
      "the test loss is (0.074     ): \n",
      "the test loss is (0.069     ): \n",
      "the test loss is (0.055     ): \n",
      "the test loss is (0.041     ): \n",
      "the test loss is (0.034     ): \n",
      "the test loss is (0.027     ): \n",
      "the test loss is (0.022     ): \n",
      "the test loss is (0.018     ): \n",
      "the test loss is (0.015     ): \n",
      "the test loss is (0.007     ): \n",
      "the test loss is (0.011     ): \n",
      "the test loss is (0.024     ): \n",
      "the test loss is (0.030     ): \n",
      "the test loss is (0.042     ): \n",
      "the trainning loss is (0.247     ): \n",
      "the trainning loss is (0.273     ): \n",
      "the trainning loss is (0.182     ): \n",
      "the trainning loss is (0.105     ): \n",
      "the trainning loss is (0.071     ): \n",
      "the trainning loss is (0.052     ): \n",
      "the trainning loss is (0.037     ): \n",
      "the trainning loss is (0.030     ): \n",
      "the trainning loss is (0.038     ): \n",
      "the trainning loss is (0.027     ): \n",
      "the trainning loss is (0.018     ): \n",
      "the trainning loss is (0.022     ): \n",
      "the trainning loss is (0.017     ): \n",
      "the trainning loss is (0.014     ): \n",
      "the trainning loss is (0.015     ): \n",
      "the trainning loss is (0.012     ): \n",
      "the trainning loss is (0.010     ): \n",
      "the trainning loss is (0.009     ): \n",
      "the trainning loss is (0.009     ): \n",
      "the trainning loss is (0.011     ): \n",
      "the trainning loss is (0.009     ): \n",
      "the trainning loss is (0.007     ): \n",
      "the trainning loss is (0.006     ): \n",
      "the trainning loss is (0.007     ): \n",
      "the trainning loss is (0.007     ): \n",
      "the trainning loss is (0.032     ): \n",
      "the test loss is (0.221     ): \n",
      "the test loss is (0.362     ): \n",
      "the test loss is (0.268     ): \n",
      "the test loss is (0.145     ): \n",
      "the test loss is (0.083     ): \n",
      "the test loss is (0.049     ): \n",
      "the test loss is (0.023     ): \n",
      "the test loss is (0.044     ): \n",
      "the test loss is (0.066     ): \n",
      "the test loss is (0.034     ): \n",
      "the test loss is (0.029     ): \n",
      "the test loss is (0.026     ): \n",
      "the test loss is (0.014     ): \n",
      "the test loss is (0.027     ): \n",
      "the test loss is (0.015     ): \n",
      "the test loss is (0.012     ): \n",
      "the test loss is (0.009     ): \n",
      "the test loss is (0.005     ): \n",
      "the test loss is (0.009     ): \n",
      "the test loss is (0.023     ): \n",
      "the test loss is (0.040     ): \n",
      "the test loss is (0.052     ): \n",
      "the test loss is (0.060     ): \n",
      "the test loss is (0.068     ): \n",
      "the test loss is (0.073     ): \n",
      "the test loss is (0.064     ): \n",
      "the test loss is (0.050     ): \n",
      "the test loss is (0.034     ): \n",
      "the test loss is (0.022     ): \n",
      "the test loss is (0.015     ): \n",
      "the test loss is (0.013     ): \n",
      "the test loss is (0.010     ): \n",
      "the test loss is (0.004     ): \n",
      "the test loss is (0.010     ): \n",
      "the test loss is (0.016     ): \n",
      "the test loss is (0.018     ): \n",
      "the test loss is (0.021     ): \n",
      "the test loss is (0.032     ): \n",
      "the trainning loss is (0.195     ): \n",
      "the trainning loss is (0.211     ): \n",
      "the trainning loss is (0.136     ): \n",
      "the trainning loss is (0.077     ): \n",
      "the trainning loss is (0.044     ): \n",
      "the trainning loss is (0.040     ): \n",
      "the trainning loss is (0.037     ): \n",
      "the trainning loss is (0.031     ): \n",
      "the trainning loss is (0.020     ): \n",
      "the trainning loss is (0.012     ): \n",
      "the trainning loss is (0.015     ): \n",
      "the trainning loss is (0.016     ): \n",
      "the trainning loss is (0.014     ): \n",
      "the trainning loss is (0.010     ): \n",
      "the trainning loss is (0.009     ): \n",
      "the trainning loss is (0.007     ): \n",
      "the trainning loss is (0.007     ): \n",
      "the trainning loss is (0.010     ): \n",
      "the trainning loss is (0.006     ): \n",
      "the trainning loss is (0.006     ): \n",
      "the trainning loss is (0.005     ): \n",
      "the trainning loss is (0.007     ): \n",
      "the trainning loss is (0.005     ): \n",
      "the trainning loss is (0.004     ): \n",
      "the trainning loss is (0.005     ): \n",
      "the trainning loss is (0.014     ): \n",
      "the test loss is (0.146     ): \n",
      "the test loss is (0.284     ): \n",
      "the test loss is (0.204     ): \n",
      "the test loss is (0.094     ): \n",
      "the test loss is (0.039     ): \n",
      "the test loss is (0.021     ): \n",
      "the test loss is (0.013     ): \n",
      "the test loss is (0.066     ): \n",
      "the test loss is (0.087     ): \n",
      "the test loss is (0.048     ): \n",
      "the test loss is (0.041     ): \n",
      "the test loss is (0.042     ): \n",
      "the test loss is (0.011     ): \n",
      "the test loss is (0.017     ): \n",
      "the test loss is (0.007     ): \n",
      "the test loss is (0.006     ): \n",
      "the test loss is (0.006     ): \n",
      "the test loss is (0.008     ): \n",
      "the test loss is (0.017     ): \n",
      "the test loss is (0.033     ): \n",
      "the test loss is (0.048     ): \n",
      "the test loss is (0.050     ): \n",
      "the test loss is (0.050     ): \n",
      "the test loss is (0.060     ): \n",
      "the test loss is (0.065     ): \n",
      "the test loss is (0.058     ): \n",
      "the test loss is (0.049     ): \n",
      "the test loss is (0.032     ): \n",
      "the test loss is (0.019     ): \n",
      "the test loss is (0.012     ): \n",
      "the test loss is (0.010     ): \n",
      "the test loss is (0.009     ): \n",
      "the test loss is (0.007     ): \n",
      "the test loss is (0.012     ): \n",
      "the test loss is (0.017     ): \n",
      "the test loss is (0.017     ): \n",
      "the test loss is (0.020     ): \n",
      "the test loss is (0.033     ): \n"
     ]
    }
   ],
   "source": [
    "print('==> Training parameters:')\n",
    "print('        start_epoch = {}'.format(start_epoch+1))\n",
    "print('        lr @epoch=0 = {}'.format(0.1))\n",
    "print('==> Starting training...')\n",
    "for epoch in range(0, 5):\n",
    "    if epoch>start_epoch:\n",
    "        train(epoch, net)\n",
    "        test(epoch, net)\n",
    "    scheduler.step()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.4155, 0.5111, 9.7233, 0.0522]], device='cuda:0',\n",
       "       grad_fn=<AddmmBackward>)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "image = Image.open('./gate_img/500.jpg')\n",
    "image = image.resize((320,240))\n",
    "plt.imshow(image)\n",
    "image_tensor = transform(image)\n",
    "image_tensor = image_tensor.view(1,3,240,320)\n",
    "output = net(image_tensor)\n",
    "output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
